ArticleBMJ open2019
Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 5 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.Frontiers in digital health · 2021Pooled it
- Identification of patients with epilepsy using automated electronic health records phenotyping.Epilepsia · 2023Trial
- Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models.Annals of clinical and translational neurology · 2026Article
- Health inequalities in outpatient neurological conditions across a large UK urban population: a retrospective observational study using automated coding.BMJ neurology open · 2026Article
- AI language model applications for early diagnosis of childhood epilepsy based on unstructured first-visit patient narratives: A cohort study.Epileptic disorders : international epilepsy journal with videotape · 2025Article
- Extracting epilepsy-related information from unstructured clinic letters using large language models.Epilepsia · 2025Article
- Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.Frontiers in neurology · 2025Article
- Research progress and implications of the application of large language model in shared decision-making in China's healthcare field.Frontiers in public health · 2025Review
- Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals.Genetics in medicine : official journal of the American College of Medical Genetics · 2024Article
- Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.Epilepsy research · 2024Article
- Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.JMIR medical informatics · 2024Article
- Annotation of epilepsy clinic letters for natural language processing.Journal of biomedical semantics · 2024Article
- NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy.Scientific reports · 2024Article
- Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence.Cancers · 2023Article
- Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.Epilepsia · 2023Article
- EEG datasets for seizure detection and prediction- A review.Epilepsia open · 2023Review
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Authors and funding
9 authors.
Funding
Abstract
objectiveRoutinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techniques to extract detailed clinical information from epilepsy clinic letters to enrich routinely collected data.
designWe used the general architecture for text engineering (GATE) framework to build an information extraction system, ExECT (extraction of epilepsy clinical text), combining rule-based and statistical techniques. We extracted nine categories of epilepsy information in addition to clinic date and date of birth across 200 clinic letters. We compared the results of our algorithm with a manual review of the letters by an epilepsy clinician.
settingDe-identified and pseudonymised epilepsy clinic letters from a Health Board serving half a million residents in Wales, UK.
resultsWe identified 1925 items of information with overall precision, recall and F1 score of 91.4%, 81.4% and 86.1%, respectively. Precision and recall for epilepsy-specific categories were: epilepsy diagnosis (88.1%, 89.0%), epilepsy type (89.8%, 79.8%), focal seizures (96.2%, 69.7%), generalised seizures (88.8%, 52.3%), seizure frequency (86.3%-53.6%), medication (96.1%, 94.0%), CT (55.6%, 58.8%), MRI (82.4%, 68.8%) and electroencephalogram (81.5%, 75.3%).
conclusionsWe have built an automated clinical text extraction system that can accurately extract epilepsy information from free text in clinic letters. This can enhance routinely collected data for research in the UK. The information extracted with ExECT such as epilepsy type, seizure frequency and neurological investigations are often missing from routinely collected data. We propose that our algorithm can bridge this data gap enabling further epilepsy research opportunities. While many of the rules in our pipeline were tailored to extract epilepsy specific information, our methods can be applied to other diseases and also can be used in clinical practice to record patient information in a structured manner.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.